Date : 2023/11/26

2023/11/27 inerview in MTK presentation

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Slide 1: Introduction

• Sorting Algorithms Overview
  • Overview of various sorting algorithms
• Objective
  • Understand the characteristics and application scenarios of different sorting algorithms

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Slide 2: Bubble Sort

• Basic Idea
  • Compare adjacent elements and swap their positions until the entire sequence is sorted
• Time Complexity
  • Average: O(n^2)
• Features
  • Simple but less efficient

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Slide 3: Insertion Sort

• Basic Idea
  • Insert unsorted elements into the correct position in the sorted part
• Time Complexity
  • Average: O(n^2)
• Features
  • Suitable for small datasets, stable sorting algorithm

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Slide 4: Selection Sort

• Basic Idea
  • Select the smallest element and place it in the sorted part
• Time Complexity
  • Average: O(n^2)
• Features
  • Simple but generally less efficient

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Slide 5: Merge Sort

• Basic Idea
  • Recursively divide the sequence into two halves, sort them, and then merge
• Time Complexity
  • Average: O(n log n)
• Features
  • Stable, suitable for large datasets

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Slide 6: Quick Sort

• Basic Idea
  • Choose a pivot, partition, and recursively sort
• Time Complexity
  • Average: O(n log n)
• Features
  • Efficient, but performance may degrade for certain datasets

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Slide 7: Heap Sort

• Basic Idea
  • Treat the sequence as a binary heap and perform sorting
• Time Complexity
  • Average: O(n log n)
• Features
  • Differs from typical comparison sorts, utilizes the heap data structure

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Slide 8: Counting Sort

• Basic Idea
  • Count occurrences, reconstruct the sequence based on counts
• Time Complexity
  • Average: O(n + k), where k is the range size
• Features
  • Suitable for small-range integer sequences

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Slide 9: Radix Sort

• Basic Idea
  • Sort numbers by digits, from the least significant to the most significant
• Time Complexity
  • Average: O(nk), where k is the maximum number of digits
• Features
  • Suitable for fixed-size integer sorting

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Slide 10: Conclusion

• Choosing the Right Sorting Algorithm
  • Based on dataset size, characteristics, and application scenarios
• Understanding the Pros and Cons
  • Key considerations in best practices